{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树的学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "boston = datasets.load_boston()\n",
    "X = boston.data\n",
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于RMSE绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KnOKk4G1+VaZN9TLq0PJnZLmt6JUSD/e82SJ85oGZSQSVHrAxD9OcAmnstQU9\np51ObkbQn78emF0mHa9+eZzUvzFiPuoCmTYcBNa9BHz8tiy3NEkMfe9G4NELIsdLTZQdy4NDPSRm\nEhJ0rfVyJ5xyvNb6Uq11kke3DTm9+ksc3eRLz7JEbs3fJBSz/yPXo9ZavKqBE+TH6y/c1Bnq9ktx\nKtOw6OdP1wL3nxg83urbv3HPESvzbgOeudJdrvnYHclp3CWR+2dly8ARp3xVyjH0Hgwc2iXbGmqA\n30+XBtX8Pm4d+twiafuYcAVw24eRpRYygfZy1gE3rdHsZ+exB5VMmPEAcMbtcj9bW4BVc+WtZ/t7\n1nF58j847RuyXF8j3xETZmlpEi992zvAtre9D/ydK4B//yCxdqA5n5JQT6y0tgL/vCu6A1K7N7KS\nasjpueVz0wGlXC+9sEwyFGb8GrjoPhGn138O/Op44BdOQ9nhfdJTsO9woKifiL3JeOgsRoyjeVlb\nnbKzvzs38twm7m06qLS2SiGpWGlulOPGXwbc8ZH0xOyIaN3us7LdcT2b2hn0O1PoyEM3IRVTHsH2\n0P2NqAAw6Vrg7O9EhlzsTlctjfIwMKV8zQAbJrPFZNGYN0I7M2vtP4DF96YmJHOgQpyMDQukj4L/\nu/jUFcD/HQVscBq+fzJUvtchhoKeakxYwFR1nPRF4OQb5O8Dq6Je5YdukanSYRJfX/si8OtJ0cU5\nonGr1d036Bit3VS5PWvc8VXNsQbTbX7po8DDp0mtj/awvTetpVJl3X6J0XY08LbBX3veZA7V7ZdG\nzrDQkYde5Ai68Z7bq9Fu4xd0k1lkyM5zO9eZvhGmM1GrI9amUdp+uzNin4q0RvMdbm6QtwR/79id\ny2W6432ZNhyI/Nwhg4KeaoyHbqo8GsbO8C6//jOpqQGIoNtdxNf/M/K8mxYBPzlSvujLnpR1DTWQ\nCg2QH8OWxcCzV7k/3JoK8XLPvlu8XvvLv/dDd94IeqVTOGrH8vY/o50hcbhKXvuLB8rA3Z3FPPDK\njgJuXgbc5eSfH3G8UwJ3gFtgLZPprIdu/gdBZXuDiBD0pd7tOXluuV8TymtplLcxU7nShLzsgnLG\nM09GT9JYSxPYgt5wwFttUmv3od9UG/tITx67nMFQVv5Z2m42vuIWSEszem5xrnTBeOh+Qber9/Uf\nI2l7hpKhEnIxrH0ROOFz3uONGC++V6YnXuONfR/aLQNi798q3vjgiTIoBCCdXFpbgFd/Ij+aghJg\ny+vusUagh4WMAAAWQUlEQVSgjXfdUW/XRutN4R+3SmbFCZ8PLjkcDeOhN9W5g3Pf9KbT2QbArSu7\nN5+/q+hQ0J0YuvGMO+uhZ+d4QyUHfAKXnefG42stQbdDKW0eulV/qC0skwQPvaURyOrgDcWmrlqm\nzfXynbQfKs317j16/2lp0I+Hyg+Bx6fJb2DfZkgfBy1tOv2OAa75S3BbTWurvMkEjUjVhdBDTzXG\nQ/d3R7dziK96VupzqGzx0ApLvamD5pXS8N4TkeVkD+zwCvqiH7vpfSb2aJbLjnLys7Ur5CbDBAD2\nbxGPxzScVneQ9WJ7Tmv/Lo1yI2Os3Gc8dDuMNHC8m9qZW9h+VcNMwYhDnyjF4fxpibGEXGr3eNfZ\nbzTZeeKl5xRYIZcGby9UU0TN46E3uvsmSqznaPPQ690//zZAPre/CN57vwf+8Y3IczYcAubdLs7O\nX2ZKRdbDVSLmx30WbW+4B3fIb8Nkbh3eJ2/QK/4ky6vmAveNk2yi2irgj1fHlx0WI/TQU02vfsDl\nj7idYWxO+W9g1fMisJ993Hn1c76Y5vW5qJ8z4MEBaVRd+rh4wX52vO+Nz1auA0ZPBza/5gq6+REU\nlALDp0pxqFdmS/rg1sUiwlteA/51l/wd77wV7P3QjbEHNcw1BnSAirUUq/HQGwMGcQgTSonXNyBK\nI3BEz9BOeoBBNebLjnJF3oQn8ntbjaKN3jCNEXSPh97k7psosZ7DDrnYHrm9LYjGWuDvt8i8f8i+\nN+8HlszxrjvzTul1PGCcpMjmFkla5+ZFwJ+ulqH/eg+SN9rCMnlbrtogIc5Hzpb5rBzgk193Sx53\nERT0dOD4K4PXT/ux/BmyslyPrN6p4z10CrB+now/OeQk4E1fMajsPPmi/+lqd2Biw6Tr5PXaZDzU\n10i2gxm047RvAH//ugwE0nBAOgDZOfPmh79nHfCDvsDpt0lxs4lf8IZTjCd/8S+AE66SjAR/iKkj\nurOgVqo55pzo2xLx0P0c/1lJQwTcN8L83t4YepBI2oJuvOp4PXRPmCQRD70huofux4yMZbPkEWD1\nX4HdH4jjMumLMuC4bgFO/rI4XgAw1XkQTLlRete++yiw9DGrTcvx4E04qGqDTC95MPFhFTsBQy6Z\nyuTrZWo63VSuk44f+6xUw2GnSlrg8KmyvM+XhjjkZKD/sfIwAES0PcWdHBFd/YJMR53vPb6mQryW\nIU73+cU/B178mtRnsTFhkoET5C2hvZF/omE805IkljXORPweeTyCfsMC4M7t3rK+Hg89SsjFEBRy\niddDtwczidlDNzH0BmkfaG123xTbE/SgVNttS4CP3pDjjjoTOO4zwDl3A2d92xVzP+MvA679q8yb\ne2Y67RnbAHnT9rdxdRH00DOV4afKKDotzfJj3L3K/RJP+Qqw5LcSD80rAr70kjRyvnEfsGeteNyA\nxJ979Qfq9jlfwgPS+GMwKXIb5gMDj4scMLtyrcQVr/gd8NwXXc9n92p5FZ97g9TkNmGSzopPEEoB\nX5qXvJrlmYpSkiF0ojO0XqcF3XpjKh0ujXV2Y16boPcBap32l2hd/oNCLvF66LbwJhpDB6RxNqvQ\n3RY0bq/JzgLke6+Ut9e18bZP/e+ObcjOkZr0Dc71dKuEGOssQbd/U10MBT3Tyc4Rj2LVX6T4V+kw\n6Ta/5LduvrBSst+nviXLu1e5I+oUloln03BAfgT5loduD7bQ7xhvQ63BfPlNHXlAeg9WrhOBX/M3\n4NNOPntn473RGDE1sePDwm1WCmk8HrrJN7d7luZYHroJGzQ3dELQE8xy8XjocQp6S6MV+qmXt0CT\nAFA8ILIQnJ2C29Io32u7AmlRQA2l9igsdQXd2FWfGkFnyCUMTL5BcoTXz5OiXiY+Ha333tfelcwZ\nwP3yHt7nhFysL58dry0eKF5eli9Fy4i+Lejb3vHG8jctkql/hHuSOO3Vb7FpE3TlNo7bD2jjodsP\n3ZYmt1ORTXOQhx5nyKXeEvRYzxHkoZtzmG1BfRPswnDmQWBnAMUs6L52jfoDXg+9s53nkgAFPQyM\nOk9y03Wr1P8edIJkoFzyQMfH2oJe74uh2+Je7Pww/ANdF/kEfczF4v3bcfQ9zsiEiXroJJLO1u03\ngp6T7x7jCblYjaKGligx9KagGHoKPfSmOvdYI+z1NfKws7/PQbQ0Sdw9IQ/dEfRe/d1r26Ekeugk\nJrKygZOuk/nBJ8oP9fI53sESotEm6FWRIRdbLEwHHtNr8OizgdEXuSM7mQyakWe4oR3D3vXi2ef4\nHgYkeQzvIBxlYuj2A9kOuRhxtzvCRAu5NAdluaTAQzdecH1Aw6rpEBftDcY0rrc0SHhEW71U4xV0\nMw5xCkMujKGHhU/cJOGPoHz29jAedl1AyMXG1OE2X/zR091xPQHJr73qj5I5YR4E+c65Gmq6vcdc\nj+LO7ZFvTn6Mh+4RdGvehF/8IRe/oOcURslDj9ND99SFidND9zSsWh56QUlwuw8g39eaj52yAXbf\nBhV7iMQW9O3vOR2ZrLcYeugkZvKLpaBXLN3pAVfQD+6SH1dUQXdCLqbeRlAX9dEXih15vYBrXwC+\n/Io7fmce4+ddRn5xx28/dsilbV1WpNDbjaxBIZc+g3yCnmBP0aBzdYaWZjd7yiPoPg89qKQwABx5\nknvNQ1b8vLA09t+Q30P39wiloJNuI79ESgqYbv+d9dA76mZ/9NmS425SHdNxYOaeRJCHDriCFyTo\nrc2RItt7UHLz0O1zxRJysWPvdtlk4+W356GPudgr6EEpi7HgF3R/KQwKOuk2srKk4ciM3ZkfpRHJ\n37mis1X+TNfx0Z2oeU66DuN1+gXOxM6DslwA70AnuUUiTkGNoknx0GM4h/HK/fa2hVyqpYSF//Ne\nMxf43FPu+sX3uUMbFg/0pup2lrZG0QESkrLTIlVWtyYDMIZOpMGz0slE8dcKmf5z4IPnI19DO1sI\ny3zZx1yUmI0kMaJ56EbYcgI8dMBbh6egVFIeA+uhJ8NDj0XQnUbH4gHAPstGf8jFL+iFfZ1+Gc7n\nNZ3sAOCc70Ufk7U9zDGFpeIc7VwhIj7oBKlk2tlMpCRAQSfApQ9K9/+WBumkZDPlRm/jp6GjMq+G\n6fcCE692S96S1BA15JLnnfoF3a5uWdhXPNCgMEkiHrqpNxTLQ8F46MUDvUPQNdc7vZ4dQfc7Iv4Q\nk02sJZ0NI88ATvsfYMgU6Qfy4cvASV+SjLCmug4PTyYUdAIcOUn+YqGzPRR7lYdrRKFMpUNBD8hy\nAbwhl8K+UrgtqCEz3iyX5noR3trKGD10S9BtWhrlDaK1Wc4bkaWT750acoviE3NAGqXP/Z7Mf/4Z\nyXQxA5cnowplDDCGTuKjsx46SQ/aslw6Crn4Bd1K6St0Qi7GQ9fa7Ukabx56U53bbhOvh27TXG+V\ngQ4IufjfSAyJ1BmyycqSsQRyC+Svo45NSYaCTuIjWT8A0j20dSzqqFHUH0O3PfRSST9tOhyZo56I\nh55XJA+c9jz01hZpuK+pkGXTqai4v+98DT5B93UsMst+oQ/J95mCTuKDHnpm4vfQ22LKJuQSRdCP\nnSa9gts6ou33ingiHnpOoVy/pVG64Vcs9cbFAeCl/wGe/byMkQuIaKusyDTDCEH3v5EYD91Xkygk\n/SQYQyfxEa0HHklPjDft99DN/9EIXLRG0c8+ISGEVU5WSO1e7yAliXjouQVy/eYGqQH0169KWuy3\nNss2rd2h3lY+B1RtkuyWghJ5GHg+Z4PrvReUenPMgcgHmCEkHjoFncTGubOBtx7o1lQskgRMOCNa\nHrpf2A0mbdGsN/0RDld5U1wTyXIx+eLvPgIsfdRZXysDTjQeBp4ztd+LxZ7tS2W578jIz9Pc6Hro\nhaXtpGmGU9AZciGxcdqtwO0bU20FiRXjQfsF23iq/rLIhsZaCW2YGLxdzM0W8VizOao2STzceOhm\nOEPdKj05cwrlbeCfs9xjJn3Re46SIZEx8uZ6d/Qgfww9Kye4SBkQGkFP2ENXSmUDWApgu9b64sRN\nIoQkHRPjDmoUzcoNHtwbEI/YFvs2Qd/rbRTtjIdeWyVec2sz8PiFMnRhU72It113fcpMEeLlT3uP\nn/RF4O0HgU/NAo4+CxgwVkI/Wbki3o2HgP/8UpbLjnby5u2KkgH13w0hqdWfjJDLLQDWAuje/BxC\nSOfp7aT39RvlXZ+T336lxsp1Mm6swQj6y7OAqV9317c0ijf/zm9l2MFcn+fccAi4fyLwqTtE1A/t\nBg7ulFK89r53bpe87vKjgQ0LpODb6d8EKt4VAb9to4R9TMivoAT4zh55SPzQyXjJzpNhEbOyXQ89\nt8jbQEoPPRKl1BAAFwH4EYD/SYpFhJDkM/5yyfc++hzv+uz8jis12nV4TMimtQlYfK+7vrkBWPFH\n4N+zpTTthCu859i5QgpqbZjvNlQernI9dIMps1wyBLh1hRSPy8qSQm9AZJoi4FSNtD7DrI9cO4ec\nDJx5F7BrpWTP2MfYUNABAL8E8C0A4XhfISSsKBXcY7f/sZEpgv/vZanAuewJCXuM7SCS2muADLz8\n+v/J8qZFUpWxdJgIM+A2ZG55TabFR0jc3HjoV/4hYHjDGOuqnDsbKBvpbSfIyQPOvANY9ofIAnM2\nIRlNK+5GUaXUxQD2aK3f62C/mUqppUqppZWVle3tSgjpbqbeAlz/snfd8E8Cwz4BfPp+4MaFMgpW\ne5z3A/FwD+6U5fefkhj5vNtl+cBO4K3fuPsXlkk8vOGAhEpyCoFxlwBjEqzIedqtcp4gJn3RHaw8\niJAIeiIe+lQAM5RS0wEUAOijlHpKa32NvZPWeg6AOQAwefJkncD1CCHdSXaOWzfcZuLVkqVSXy0x\n9pIhwE3/ESEvLAWWPCIx6o/elI5CL39LYubHXSle+5SZwLp/uOfzx9tTQU8PuWit7wRwJwAopc4E\ncJtfzAkhIeTSB2W68Eci6CpLOvqc7jSjTb4eWP4M8NebgIU/ADa/ChxzHnDZw5Hpj0Bk56BUwCwX\nQkiP5sw7gRFTJUTjx6x74xcyHXeJt5qhHc9OtYd+8S+AYy9IrQ1JIikdi7TWrzIHnZAeRlaW1M8P\n6jXcdwTwmcfdZb/op5OHPvl6qSIZAuihE0K6hgmXA8OnApv+HTnAiS3o+eFokEwHKOiEkK6j90Bg\n4hci19tjdx51ZndZ4+W2jfEPapGmUNAJId1Pdg7w5YXiuacq3BHUSSnDoaATQlLDkICUSJIQrLZI\nCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEh\ngYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJOCCEhgYJO\nCCEhgYJOCCEhgYJOCCEhIW5BV0oNVUotUkqtUUqtVkrdkkzDCCGExEZOAsc2A/im1nqZUqo3gPeU\nUgu01muSZBshhJAYiNtD11rv1Fovc+YPAlgL4MhkGUYIISQ2khJDV0qNAHAigHeScT5CCCGxk7Cg\nK6WKAcwFcKvW+kDA9plKqaVKqaWVlZWJXo4QQkgUEhJ0pVQuRMyf1lr/JWgfrfUcrfVkrfXk/v37\nJ3I5Qggh7ZBIlosC8CiAtVrr+5JnEiGEkHhIxEOfCuBaAGcrpZY7f9OTZBchhJAYiTttUWv9BgCV\nRFsIIYQkAHuKEkJISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCg\nE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JI\nSKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISKCgE0JISEhI0JVS05RS\n65VSG5VSs5JlFCGEkNiJW9CVUtkAfgPgQgDjAFyllBqXLMMIIYTERiIe+hQAG7XWm7XWjQD+COCS\n5JhFCCEkVnISOPZIANus5QoAn0jMnGBm/3011uw40BWnJoSQbmHc4D743qfHd+k1urxRVCk1Uym1\nVCm1tLKysqsvRwghPZZEPPTtAIZay0OcdR601nMAzAGAyZMn63gu1NVPNUIICQOJeOjvAhillBqp\nlMoD8HkALybHLEIIIbESt4eutW5WSn0NwL8AZAN4TGu9OmmWEUIIiYlEQi7QWs8DMC9JthBCCEkA\n9hQlhJCQQEEnhJCQQEEnhJCQQEEnhJCQQEEnhJCQoLSOq69PfBdTqhLAR3Ec2g/A3iSbk2zS3Uba\nlxjpbh+Q/jbSvvgZrrXu39FO3Sro8aKUWqq1npxqO9oj3W2kfYmR7vYB6W8j7et6GHIhhJCQQEEn\nhJCQkCmCPifVBnSCdLeR9iVGutsHpL+NtK+LyYgYOiGEkI7JFA+dEEJIB6S9oKfjQNRKqa1KqQ+U\nUsuVUkuddWVKqQVKqQ3OtG832vOYUmqPUmqVtS6qPUqpO537uV4pdUEKbfy+Umq7cx+XK6Wmp8pG\npdRQpdQipdQapdRqpdQtzvq0uI/t2JcW91ApVaCUWqKUWuHYN9tZny73L5p9aXH/kobWOm3/IGV5\nNwE4CkAegBUAxqWBXVsB9POt+xmAWc78LAA/7UZ7zgAwCcCqjuyBDOi9AkA+gJHO/c1OkY3fB3Bb\nwL7dbiOAQQAmOfO9AXzo2JEW97Ed+9LiHgJQAIqd+VwA7wA4JY3uXzT70uL+Jesv3T30TBqI+hIA\nTzjzTwC4tLsurLV+HcC+TtpzCYA/aq0btNZbAGyE3OdU2BiNbrdRa71Ta73MmT8IYC1k3Ny0uI/t\n2BeN7rZPa60POYu5zp9G+ty/aPZFIyW/k0RJd0EPGoi6vS9xd6EBvKKUek8pNdNZN1BrvdOZ3wVg\nYGpMayOaPel2T29WSq10QjLmdTylNiqlRgA4EeLFpd199NkHpMk9VEplK6WWA9gDYIHWOq3uXxT7\ngDS5f8kg3QU9XTlNaz0RwIUA/lspdYa9Ucs7W9qkD6WbPRYPQcJpEwHsBHBvas0BlFLFAOYCuFVr\nfcDelg73McC+tLmHWusW53cxBMAUpdQE3/aU3r8o9qXN/UsG6S7onRqIurvRWm93pnsAvAB5Fdut\nlBoEAM50T+osBNqxJ23uqdZ6t/MjawXwCNxX2pTYqJTKhYjl01rrvzir0+Y+BtmXbvfQsakawCIA\n05BG9y/IvnS8f4mQ7oKedgNRK6V6KaV6m3kA5wNY5dh1nbPbdQD+lhoL24hmz4sAPq+UyldKjQQw\nCsCSFNhnfuCGyyD3EUiBjUopBeBRAGu11vdZm9LiPkazL13uoVKqv1Kq1JkvBHAegHVIn/sXaF+6\n3L+kkepW2Y7+AEyHtOhvAvDtNLDnKEjr9woAq41NAMoB/BvABgCvACjrRpuehbwuNkFifTe0Zw+A\nbzv3cz2AC1No45MAPgCwEvIDGpQqGwGcBgkHrASw3Pmbni73sR370uIeAjgewPuOHasAfNdZny73\nL5p9aXH/kvXHnqKEEBIS0j3kQgghpJNQ0AkhJCRQ0AkhJCRQ0AkhJCRQ0AkhJCRQ0AkhJCRQ0Akh\nJCRQ0AkhJCT8fwdIGAHW32zDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f62a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def plot_learning_curve(algo, X_train, X_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1, len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "    \n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(mean_squared_error(y_train[:i], y_train_predict))\n",
    "    \n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(mean_squared_error(y_test, y_test_predict))\n",
    "        \n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               np.sqrt(train_score), label=\"train\")\n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               np.sqrt(test_score), label=\"test\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_learning_curve(DecisionTreeRegressor(), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于R^2值绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gC9tF2CG882gfwHAIP386nrUxFnBZMLa/DlWXLVOquDfKMeMu3nVpsSdcquRg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119637128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def plot_learning_curve_r2(algo, X_train, X_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1, len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "    \n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(r2_score(y_train[:i], y_train_predict))\n",
    "    \n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(r2_score(y_test, y_test_predict))\n",
    "        \n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               train_score, label=\"train\")\n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               test_score, label=\"test\")\n",
    "    plt.legend()\n",
    "    plt.axis([0, len(X_train)+1, -0.1, 1.1])\n",
    "    plt.show()\n",
    "    \n",
    "plot_learning_curve_r2(DecisionTreeRegressor(), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以max_depth参数为例，看不同参数学习曲线的不同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Se7x5XA5y0tzsqqjl9TV7cTqEvVX1jC7K4JYzS3ny4+1t1qJZvqOy6fbeqnqKs71N9+t9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113332d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=1), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vrZjOff0/dhEi4l6odDQZIT3uaqianCrsAOff6GybkPt7Tjgi9XkiBvu3meUx\nkyHkirhtlosV92hnqi3T7mTSVIw14t7d6mTLVIotM5LwOZF0po7Qbueuzdp5bqy4K2XScC2BAWbL\n+HymPbEuM7ZC+VPfty+CZeb76y1lPUIRcS9UOprMyE936qKbcEVvse6Lg06E/3jRLN98rBHrFkfc\nqyamj7hTxN2xZQIhejqqysc67d1rfnThDB2qki1TnPhD5jFTKQHbme4eP2GxtgwkLxLgfH8GSCBs\nkguU6v9FQikTvUvkLgwrHU3J0aD5QikYd2iy3otX3EMecQ+UQlm9WbbZMv5g6o/KinuLE+mXVEnk\nPpKwkXGmInM2Ys8YuTui7vbSrb0yEAJh47cr38AuEqNI3KX8QKHSsTf/4m5x32q3bIdguUl5dHvu\nYOq424qQ0U4nEgulXgQqxplHK+7ePHfLKOnEGnHYyD2eoTy0jdgzRu7OxSElcj8AcfeHzR1iLDKw\ni4SIuzDsdOyF0trBeW+fz/iV8W6TQVA10UT1NuKeeY55nHikS9y7zK25P5hq3/RE7s4dQLjKuUi4\nUtjA/CBtATKheOgR9wzVPXvSYNNcvG0qJHg89wOJ3EPGlolFBvY+pTWjxiIUW6YQiUchsn/wIndI\njkht2Z5MJbMR98Ir4ZLfmVS11nfMOmvL+IKpEb4VdzshQ7jSuVBUmdvyaLuxd2KdpsM1WOLUFwmk\n5jALhYktCteXLeMe92Cx/2fIX+QeKDGBgs8/cHHf8frAj19E5BS5K6XOVUqtVUptUEpdn2Gf05RS\nryql3lRKPZXfZo4ybG9+2SBF7mDEPdYNe99KdpraiDxUYUTYH0zOwhRpdfLcQ6lZNePmwJRjk89t\n9G8LiEHy/W87E/7yUfj+DJMbLRQ+tiM1lkHcbYdq287e2xKxZMSeL8/dBiViy/RJn+KulPIDPwfO\nA+YClyml5nr2qQZuBi7UWh8GfHAQ2jp6sDMgDWrkHoQdr5ov+tTjzTobubsj87Jak0tsa7f7PZF7\naY0ZtDLuMPO8ZIx5nH2uGRiFgsM/CAs/DpOOhFV/MxG8e1CLULjYzvdoGk8dskfu7g5VX75smRLT\npgHbMtXms4yCSWRysWUWARu01hsBlFJ3ARcBq1z7fAi4V2u9BUBrneY/LeSMLbU72JH7VictctrJ\n5rF6qvkRVrpy55UyA1BW3pN8ndtzt5kzH3sQVt0PtTPM8wv+z3nvk2DSUckLwlM3whPfhuoBDGIR\nhh6b356Y/mQwAAAgAElEQVSuwxTSR+6JhOnXcadC+vNly4SNMCvfwG0ZML57ZT8GQBUhudgyk4Ct\nrueNzjo3s4AapdSTSqkVSqmP5KuBo5ImZ3RoVY7DqgeC/bHVTE8OKz/4DPjc6t4Dow55V9Jz9QdT\nbRnb4VpWa7x6r48+/eTUSP/UL8Bx1yQvYMLQ0t1hxLGrxYxC1jp7mqrtSM0k7jZydxf8st+VTJF7\nPlIh4wfQoQpJa+afX4IfzYOfLYJNz2Z+nZtX/gB3f7z/xx5i8tWhGgCOBs4ESoEXlFJLtdbr3Dsp\npRYDiwGmTp2ap0OPQDY8ZoS97pDBO4YV93FzkuuUSqY2urFVKO3r3GI9kNGG5fVGFLo7TLqlMDQs\nvQX+eZ3pI+luN7WEZl8Aa/8BH77PXNy9WKGOpMljB5ct4xb3SLLj3P5/85oK6QxismMw+oMdYGfb\nvfFJc4HbsxY2P2/uNPtiy1LzGy1wchH3bYC7YtBkZ52bRqBJa90OtCulngaOAFLEXWt9K3ArwMKF\nC9PU8xzF7NucLMe78Sk47KLBzSaxKW422yUb7n3ctkygdGBttO/XvhtCYs/knXUPG9vhiH8z6acP\nXGui8w2Pmu1NG5IlcNf+wzxuXZ5e3G1HarpBSrFuV5TuGsFqrZxBS4XsGvggJhuY2BTOeASmHgtr\nmnKfCjIezZw9VEDkIu7LgZlKqekYUb8U47G7uR/4mVIqAISAY4Ef5bOhI5r2PfDLU1JzhWdfMLjH\ntClu6SJ1L+4Rp75gsuM1OMAaIfaY7XsGVkBKyM7y203NoCP+zVT3fOOvZv38D5oicZueNQLpJpPg\nWhFLZ8ukE3z3a+Kx9KmQ/gMpP1DiGqE6gO+fFXf7eeLd5m4gXNEPcY8URYdsn+KutY4ppa4FHgb8\nwB1a6zeVUlc722/RWq9WSv0TeB1IALdrrVcOZsOLkg2PmZmT9qwz2QU+v7E5tDZlTD98H1SMNz80\n2zE5WNiCT+U5iLs7Ove7xD1QOrBjlzu303amJyG/xF0RtY28P/QXmHUOLPmCESZvIbBghv9lNnHP\nJIZW+BLR9KmQAy0cBk4KryPuA7lI2O+u/Tyx7uR3OtPFyks8amYnS8QLej7YnDx3rfUSYIln3S2e\n5zcCN+avaSOMh66DF28xFR6nn2ysCZ1wbvEicPCZ6W+LBws7Sq88R9/S5hf7Q+YHGygZeOTutmWE\n/BPrTnaEujvCwfk/prEVMgluLpG7HVjU85po8nGwUiEHHLlbcXfZMgEbueco7u7aTL4BBjhDgIxQ\nHQreecMI+5Efhgt+eGBV8fKFtYBysWXA1J5p35UUiVD5wCN32xHWnmPGrNYmuyZceWDCMFqId7sE\n1hEim6HidzxrHU99TZ+Re5v5P7jv4qzgVzSYqe56XmMj99ggpEI6QcZAUyHDnsjd1ksKV/XPlgEj\n8pnOWwEwMmvL7G9M1ikvBJb/ykQMZ3+rMITdTS62DJjBH+AS94qBR+6hMvP6fZv73hdg6c1w48Hw\ns2Myj5QUkrg9YSvy1sLwh3oLOzjT1DnsfNPMTQrJ863j8OIvYdltsHutyRix9YQqPamzaVMh8zVC\n1RYO6xqYLRMoBVQySo9FksXwuvvRoep+LFBGZuT+I2e05NcLpMxspMVEN4U4KW9FDtkykBx5an+k\n4cqBR+4As8+Hl39rqk2GK0xUqBOANnn1cy8y+8W64fmfmuXmzXDraXD2N80+QnrcWSxW5G1QkWka\nu4RL8O/5lEmRvfhXqfbNP68zj9NPMWmDdj6ByQth69LU40NqVciU8gNZptLri0BJcmLtgdgyPp+5\n6+xuN4OtEtGkLdPUX1umsDtVR2bkXmgk4qmeYyFRUt2//aKd5nHCAhg/b+DHPfOrJlpa/XdY9QCs\nedDMGbvyPvjbfyRnelp+u5nU+PK7Ydxc2PUm3PPJzCVohdQO1R7P3YmWM1kZ7mi+oyk5yCwegVnn\nwXWb4AtvmdHM+zabqNzeeR3zSfjKLvjog6nHdKdCuu8MDiTF133nO9C7YBul2/TNgXSoQsGnQxao\n4owwdAH3quf6Q7O2jPXq3/vzAztu9RT43CpTS96dA717Ldx8HNx6urk7aFoPM882kfrYQ+G1u0z5\ngjUPmlnsn/x/RrAOe7+ZKrCsLvtn0hoe/Rq07TY2Wa4dysWEFXet03eoWnzBpMAlXBfLSKsrDzxq\nRNTedZbWwNZlZrnDuQDbvhAbSfeInysVMl9jNtzR+kCzbmzkHnP1R4QrB+C5i7iPHu75JFSO7z01\nXiKRzCsvFP7zlWR0nAtn3GCyW2afl782WKvHzdjZ8N5fmIgeYMoiOP3LRhyqp8DJn4PnbzK52r6A\n6agGeO4n5vHID8Mrvzc53dteho/8zQwXX/8ILH7SlDC2+x56Acx5d/4+T6EQd9kidjng6lC1lNUm\nR5ZaWyYRN4W1bBRrM6Qs4credkRPaqyn9rs7FTJfuNsy0Hx5mxnTc+Fz5gWOduSW3lgktszIFnd3\n4aKhYOeb6cuJ6rjx+gqJ2hn9y6WvOQg+cv/gtcfNEZeav3T4/Oai0N2WFKAr/2k69574X9jl1LOz\nA3d2rYGnvpd8vTulr8BvqwdMj+ft6lh1d6haSt3i7kTu9pzaKDaWRtzd+AK9Lxzui4svz7+/lMh9\ngB2zoQrzPei58LlmF+tuSx90uCmSDtUCU5w8M9QzrsSj6UeuJWKFF7kXM9YftQJUdzDMv9hE9t5c\nZW8GhNtXHaninhK5e7Jl3ILo7uC3kbs9pz22jEfcvVMohiqSlovdL+ZYQjqe/+AqxXM/EFumzWPL\nOJ8rl1x3dypkATOyxT3bvJ1Pfd/0+OeTRDT91bzAR7IVHfa22kbhVnD84d6dYt7BN+7nBf7jHDBu\n8bHLPdG1S2zdJaVth2rEFblrbV6fLXJ3P3dH7vZOIN+RuzuNcqC2jA0O4p4OVcjNd5cO1QIgU+Su\nNTzxHbOcz3RJO9q01/HiErnnE9sh1t1msjDsQJJAqHfk1SuSH+G2TDzmpJTizFjk8pUhVRxLXZlS\nVox7xE071kU0NVr2iru7Qqh7vtUe4cyzxOTFlnG+P+4Lnz0vuWTMuEeoFjCjM3KPdgzO8eKZIvdE\n4aZCFiM28upu720L9LJh3GIeG/m2jPsz2awZ5U/eObojd/cE7D22TEtynbUuskXubpump0M1mszC\nybvn7rZlDsBzj7SlXvjCOUbu9m4GCv7Ob2SLuzdyf/J78PPjzEQFFpu3nQ8SGTz3QuxQLWbcnntK\n5BhORq0Wt9jHI6Mgcnd9/+LdvW0Vb7aMxYq7++LX2ez45i4R7WXLuMTdbcvYcQj5DmrckftAR7qG\nrS3j6mx2d6hmw50yeqDfn0SakcLZ6GrpV4bbyFYcb+bKk/8Lu1enRvS7VufvePFYelsmIbZMXunx\n3NtSI8d0nXdeMXf/eAs8T3lAuO8cbfXHTNFuSoeq15YhOZev+7ym61C1WLGNdbsGCBWgLROuBDT8\n+rzk+9iL1NtPw2/eDS//Dh79ulnX3Q43H2/KL/zINXAv3m1GTH99DKz9p1n3yh/gV+fAw1+G7x8M\nP5htSjb89Ghodk1od/cn4Ju1pjRJLqz4DXx3Ctx+Zs4fc+SJu/tqmMmW2ft2cnlnHisTZ+xQjUmH\naj7p8dzbPbNCpfmxRzxinmLLFPZt9YCIeSJ3r62SyZbRnmwZSI5SzbVD1ecHVGrhMrctc96N8IEc\nxSwTDYfBmV+DU/4Hxs8f2HvMu9gz7V8oOUPThkdh0zOw7FYzYA6gZbtJsX35d9D2TvJ1sUhygh2r\nI9teNqUY3nrcfDfbdxlhbtoAu9ckX7v5OfPoXpeN3WvN42lfyvljjjxxd98qLf2FGYnoxX1C3RbN\nAR87iy0jkXv+CDkDaTr3pc/WcOOeq9XaMr6gqYszIm0Z12d67U+mImlKholruXaGmXIP0kfu9twF\nstgy7shdKaekcCR1aL/l2MUmZfVA8PnNQLYzvjzwNMsxk+B0l0i6bZmWHc7jdldaqKMRLZ5ihO7g\n0ZtC2roDph5vcubt69z9Ge6spFyItEDlRDMBS46MbHHv3At3XgI3HgJ7NybX73HN/pfI00CERMKI\neMYOVRH3vGFvodt2ps/WcOOOtOLRZLQfCI1QW8b1mVb8Bra9lGrLuM9RyRj4xCPmQufNcwdX5O4S\n0WzZMuBMYB1Nvl++O1TzhY3UwZyTQNhE8zGnD66jydzlJeLJc+K1ed3+t1fcbeARrky+zm5LJJJ9\nQTmLe2vvc98HI1DcHXE97Ysw/VTY/rIZNr/lxWT0bG9xIH8FqOxFIlMqpIh7/rCC0rrTk62RxpZp\n9dxGd7ebH4k/PDJtmXR3I25BTyf0vkCquNvfSYf13NNE7nafsMeD9wdTZ3rKt+eeL9xCGQiZuw5v\nfwKkDpbzYs8P9BZ3ewz3RcRuc1uDkRydAxF3kl+qigY45QvJ9bGuZD60W9zzFbm7BzZoz9zf0qGa\nX+yPMB7pO3J3z/YUjzidsOXJGYlGGunuRjJly9iI3OdLtWUqGsyyjUy9U+T5AqaGEhiLLOVYYWcQ\n0yClQuaLFDvPuXi5hdjiLqLmpSNN5O7OziqpSj1OuguARO79wF0MaNpJpggVmH+EnQos2m7EVvnz\n9wNPN/u7RSL3/OKOsPry3N30smVGYuSe5jNlEnd7p+MLmO/opudg5d2mUqYvCKsfSN0PkhFu1UTz\n3Bu5B0Lw+l/gN+9xjleg4h5Kk8Lp/SzQh7g3pe7nfoSkLZNxHzWo4l6g90wHgLuWhlKw4ENmUuD9\n21JzVMOV5sedt8jdk//qvv2V2jL5xf0j7Ctbxo21ZULl0N0xemyZQIYh+ym2TAxeu9M8P/EzZjaz\nnSvN3e7UE1Lf76xvwNg5Jqtk9vmp2864Aba8YJaDZaZTsRDx2jKQ3paJtGa2TuydTdWk5D4p4u6N\n3J19rC1TNamf4p7mziILI1DcPfWrwQzW2Pd26n4lVWaQRr49d3cberZJh2pecQt6X3nubuLdJkuh\negr4m0e5LaNctdb9TqnfTqg7pO+MlqM/Zh6nHtt72/yLDzwjZijwdqhChsi9pe/IvWpiUuhzitxb\nkq/LNRVbbBnSp2CV1cG+TWZ5/OHmMdrlRCx59tyh9+2+pELml1CGyN07YtE7DaAdxBQqN9HsiLRl\n+hJ316QdtmyD7VCNdhb0hM95Ja3nnkY8s9kyPRH4RLNPrDtp/dr3y2bLVE00pVD6CjATCRF3oHeJ\nU0gV90MvMI/tu8wXPV+5zvFskbuUH8gr7i95uttrS2VD6nO3LeMPm/r7f73SjEj82SJYesvgtRlg\n3SO5l7t4+2m4/1rY8Fj/jpHWlnGdF5uLnjIjk9/YMtEOY6WMBlIGXzm/TW/nMGQXd0ulI+7e0gXh\nSgi7asP3EvdJ5rGvibmj7YAWcU9vy9Qllw86MbnsDw2NLaMLeA7VYqS0FiYeBdVTocE1HNzboTpm\nSurzv37UXNTDleb70b4L3rzXDEbZsxbWLhm8Nu/fBnd+MDnDVF8su9XMKPVCP6czzCVY8YdTBd/n\nN9/R0RS5p/uc/e1QBXMuy+tM/4237ksukbv7eSbsdulQ9ZQ4hVRxL62Bi++AivFw/zWDY8uki9zF\nlskf/gAsfiLNepct8/7bYepxZiCPjsOzP0puO+aT8NB1Zln54apn4PfvM17o799vRhee8J+w4tcw\n971mRPOFNw28vXd/HMrHmuVcO9AGWlY2ndXk9eH9QU/kHkhG7u7fykgm3ZyuGTtU0/zP7DgJdy57\n6/bUfXp1qHrFfULq80yIuDvE00wS4K5+V1IF8z5glv3BwUmF9P7AZLKOoaEnbzsAh3/QLJ95gxnI\nZMX92H+HmmnJfcOVTnpfOexZD9tWmPV/u9o8bn3RPB6IuG94LHkXke77tuR/TI2SYAlcdLPp8B2o\nuKfb35sV5LVl3B2qoyVyT0dGzz1Ntky4EjoiqdF5i1fcs3SoBsuShdtyFnfJljGPmWyZFK8tmJoe\neUDHdadCpslzl8h98OmZbcjTsZqu8JV34EqwLDmfaD7R2hnl6AhEuvTLl+4wQUdHkxH56inJ71B/\nO33Tibs3cg+k89xF3JO2jAKcgYg2W0b5UstJhyvM2JkUcXdqyChfcm7ZnhG9PvM+7s5R+93rU9yd\n74547mlsmemnwoQjYNLC1A4Of2CQBjGli9xH3qkuOKxgeztW04l7wJMhESrPn0XnJhYxAYTtbPMK\nbSJhjjvjNPO8Z/7TfkbuHXvh+Z+m77DtK3K3tkx3++jpUE2H7VCtGGcey8cmbZmKhvT7uq0XG7lX\njE+uswJe0QBo0znaI+7OPn2VIBhMW0YpdS7wE8AP3K61/m6G/Y4BXgAu1Vrf3a+W5It02TJ1B8NV\nT/fe1xfMo+fu+hF6f8ASuQ8N7jQ/N+mqGrptGehdACtfdHuq/3nF2j63IuC1Y3IV9wc+DWsehElH\n997mjf6lQzU9NnKvnGDu4ionwIbHjfhOOsr0xVhCZcnX2O+QLRE8ZlKy7IXdVjXRvP6Wk82YmwkL\nktv+/lljzaE9pUucZasn+RZ3pZQf+DlwFtAILFdKPaC1XpVmv+8Bj/SrBfmmP5ME5NNz945QTWmT\nZMsMCZlsGfe572XLOM/7ili1Tt8J1xdeUfdG0fa5bYfNk7Y/aPvYvgfuuxqa1ptbfH/IBCdjZxkR\nWvuQ2W/bit5BSy9xT9Ohauf/DQ7SRa4QuezPyQlJAA46AY76CMy50NR1rz3YlGAorTEd7GuXmBG5\na/4O00+BlffC/A9Cw3xYdJX5X1dPMdtswbq6Q8yo3Zlnw7Jfmgvo+PnmfSonwMn/DW1OWrZy3907\n3zX7nasYZ7LD+kEuirMI2KC13miOpe4CLgJWefb7NHAPcEy/WpBv0tkymbC3o/kgky2jtdSWGSrs\n/9xry7hF2UbIdp9cI/dEbGB1Ury5z95gIuaJ3DPZMttfgQ3/gnFzzZ+dH3XTc+YY5fVGOCItvQfn\necXdFk6zKH/yIjSaIvfZ56Y+L62BC39qlmeeZR6PXZzcPuWY1MdD3pXcdv730x/D54NTPm+WL0qT\n1nrmV/vX5n6Qi7hPAlzzQ9EIpIw7VkpNAt4HnE4WcVdKLQYWA0yd2r+rUM6ks2Uy4Q8d2GTZWptb\nrNoZnlRI17LthBFbZvCx//Ns//ueyL2f4h7vHpi4Rzzi7hVaG6n3RO5W1D0lpO1+7/slTDg8/bGe\n/gE8/i2TQNDSaHzetp1w5BWp+53zndTvo2+UivsIJ19ewY+B67TWCZXl1lVrfStwK8DChQt1xh0P\nhB5xz+GHeKC2zFuPwx8+AP/1euodgPsH3DNpgXSoDjo9tkwu4u7sa0W9L1smFhmYL98rcvfaMh4/\n1T6336GY57l7DlEvx18Lk4+B2ulQPi55m+/9LUw8MvW5L+AS91HcoTrCyEXctwHuoX6TnXVuFgJ3\nOcJeD5yvlIpprf+Wl1b2B/vjyKWO9IHaMm07AW06TzINYrJzU0rkPvhk6lB1Y0XUXmwDHpGH9B3t\nAw0CvGluvWwZR7RDZeY70ityt+LeldredARLYMap/W+jz5+8CEnkPmLIJZxcDsxUSk1XSoWAS4EH\n3DtoradrradpracBdwP/MSzCDv20ZTyR+6ZnTSdJrlhLJ9qZKuju5Z7IXcR90OlJhcwigD32h+2b\nSSPupdW9XzfQ8sDeyN1ry9j39YedYmZdqesTUZMuadcPhvj6AsnvskTuI4Y+I3etdUwpdS3wMCYV\n8g6t9ZtKqaud7YNcbamfxLsx5UxzEFNvhLb0F7BrNcx7f27HsjnF0c7MtWUkch86+uO5W/G0HatW\n1EIV6a2Pgc636vXcvZlU9n3twCI7k1csYr4zOm6+Wz22TB816weC+7cikfuIISfPXWu9BFjiWZdW\n1LXWHzvwZh0AiWhqOdNs+IOpKYyxrtyr9oFL3D1lO2PpIndJhRx0fM7IwGzibq0br4cdcol7utcP\ntHpoL8/dK+5ORN4TuUec74xTBbCr2azrsWWyeO4DxR14SOQ+Yhh5vXzxaO5ZDd6UsWhX/7JnemyZ\nriypkE62jNgyQ4N3gE4mvJGwLRrlTRO0DNSW8XruvWwZG7k74h7v7p37Hu823zHIzW7sL+7AQyL3\nEUPxi/uq+6HdNZdhf1LWvJMkx3IU97UPmWnIUiL3DLaM7bBVxX+qiwLvAJ1MxD2ee9A14jDdxWGg\nHap95rlbzz1k2hLrSratZyJwZxKIQMnABlL1hdgyI5LiVpxIK/zlI/DqH5Pr4t25Rzd+T+Ew+8PK\nVuNda3PMZbemdqja9wmWZ7BlJHIfEoKluVkXx/27qQs/82zzvC9bZqCzNvXy3DN0qAZKHFum2zWw\nqSJ57FhkcPx28ETuYsuMFIrbCLY/nK79yXXxWO7i7gukRtk2Eo91gj9DHYd41Lyma3+ayF2ZdDTp\nUB0+3vOT9MO0r7g39f8yfj5c55pX1w67D1WYAlpe8uW5eztme3WoRpKC3xO5R53IfZCiaoncRyTF\nLe42cnb7mvFIP2wZTyqkjc66OzIX6XEf0+5vI3d/MFnE3yKR+9Ay65z06w85M/vrAk6tlkzVIQcq\n7r3y3L2Fw7ypkJHkd9Kd2TOYkbt0qI5IilzcncjZHR117c+9epovaCJrWxQq5orE+zpmpC0pAtFO\n83pf0IhEuvIDki1T+ITKjRWS7v//+HdMRL/hMTjps2afl38HU483GS2LPmX2i3XDG381s0Bte7nv\nEarujl1/yEToMU+HaszluQ8G9rvpD+XWGS0UBcWtOD1C66qH3NkMJWkGoaTDVo6MR82X2h2JZzym\n+25BJ9f5Q+b9/CHSlh+QDtXC55zvmKJcdtamCQtMCd2XfgU734Dnb4Idr8HEBSaIeOlXsP1lk8li\nxX3jE3D/f5i5ercsNaUA3GTrUA2UmAtFL1vGivtgee5O5N7PkrJCYVPcitMjtO7IvTn9CMN02BIF\n7gjc/Zj2mPaC0urpUI2a9/M76WybXzDCbjtaxZYpfI68wtTttn02Ew6HEz+T3G4nY4i0JAOKlu2p\n1ktns3ls3mLuCtt2p/YBZRqhGihxAoxuly1TkdxnKCJ3EfcRRfGK+/M/NdOTgUlLfOQrZjaafkXu\njrjHo+bPdn5G03SoWdx3C94OVX/Q/G1dBr8+Fx7/tnSoFiPuuvBuYbYTMLgnTW7blSruVvTtxA6R\n/anTPGYaoerur/HaMvHoIGfLON/NkIj7SKJ4bZmXfw971prlpvXw/HpYs6R/kbv94SZiyRGAkLst\nY/30WBckyh3PPZz8gW98EuZeZJYlci8e3GUM0gmqW9zR5v9t+23seneKbVldUuzTdaj6w+a1NhXS\nm+duR6iW1efl4/VCiS0zEimeyH3NEvj6GHO7C+nT1fa+ZX4YuUbuPpfnHnWLey4dqq3JNtjCYXYA\njb2tbtspkXsx4p70I11abYq4A+jkdyHdZMdltcllWzvGEutOLVUcj6QpA+xE88HBtmUqsu8nFBXF\nI+6v/ME87njNPGazTnKO3F2ee8wVrecSuSei5i7BruuxZUL0dLS27jAV/UCyZYqJgCtyz0ncST5P\nK+51qc9T5tvtcl1MSpIDliBNnvsgi/tgzSMrDAvFI+7eGY3SRe6WnCN3R9xX3gs/np9cn+293cJv\n22Tz3K0t496uZbKOosNty6QbMxFpy5+4x12Ru83Y8naoxiLmznLQPHfnuzma5k8dBRSP4mhXSmE8\n1tu7LB+XXM45cncilke/lro+l8jduy7aaW6bvWJg30tsmeLBXRc+XS0Xd7ZMz7p+iLt7lKq7o9R2\nqKYrHDYUkbuMTh1RFI+4u0d6prNk3L5mfyN3L7mkQnrXRTvNj8Pvia72bnSOJeJeNLhtmXSktWVa\nUh/dZI3cI66LiXM8e+cYcot7ZPDE3SLiPqIoHnHvsWVUetvE/cXvr+fuJZcOVUuowqyLdZraH94R\nfkucmc8lci8e+pr0I9LSuyBYv2wZV657rNt1MQkn3x88hcMG0ZaxyQRSemBEUUTi7so66U4jvu6o\n44Aj92zi7tlWWuuyZUpTBWHRYqnnXoz0dHBmENOOpt71Z7KKu3NXGfJM8QeeyL0k9T1sh2q003z/\nB6twWM8UexK5jySKR9wTLpFMZ8sESgDHHy0Zk9t7DiRyd+fDg/nhJmLmB+m1ZcbNSS5L5F485DJd\nnxdbQ8ZbSwZMAACuEacZPHcbwVtxD5aa742N5ActcnfuRiVyH1EUT35ejy3jS2/LBMvg358zk1zn\nGiWnFXeV/LK37jQR2pjJyc3RDhNRxZ1h4pOPgR2vmmguWJr6nqWufgCJ3IsHd955rvR47llsmZCr\nnIAlFoFSR1R7bBnnAuELOIPinPccLM+9R9wlch9JFKm4eyLrw94PZ30TqqdAw2G5v2c6W6a0Blbe\nAzPPgfsWm3VXPwfj55nlaCdUjINPPgbhKnjVyb+Pdzueuyu6cnv/Iu7FQ1+2TDoirSaLK91dX2mN\nebSR+4u/NNUkfX5TvqBygnM857g7VyZHrfpDyYF7gxa5iy0zEikeW8Z67lr3vvX94K+NsPcXf5pr\n25GXm8cHrk2u2+ea1CHaae4SymrN60OuUX3uyN0fTq3VIbZM8dBjy2S4+Fts305JtTNi2dopZcnH\nYHlydqi6Q8z61/8MD/4XPPBpaGmEyvFmffU08z3ZtQpqZzjrpsCmZ8zyYJUHEFtmRFJ8kbtO9G8S\n62y4f6iWU6832QPLb0uu63DN0RrtSI1wUsS9JLVzLOT6sUjkXjy48869VE2Czn3J5VjEXOj3rDe1\nhMBE4nvfgqqJRvR9fvjEv6BmGpx6nRFR5TN9NToOVY7tN/lo+GKjWW+F9uOPwFuPm76e2ecPzuc9\n48um+N60kwbn/YVhoXjE3ea560T2EaT9wd4OuwmUmC/58ttg8iJoXAbte5LbbVaMxT1kO1iWHBAS\nCC/5XJwAAAyKSURBVKdGQhK5Fw+TF8KCy03JXzBZT/GoEfBZ58BTm4xgz3ufqdkeLIPVDyQj7ElH\nwf6tphb8Xueuz75XSVX2Y4fKej+f8+68fbS0jJ8P//7s4B5DGHKKR9z1IIh7IGyidxuJgbFapp1k\nxPjg02Hnm6aUsCXaASWui4K72JJ7dvpASarwS/mB4qG0Bt57c/L5+Tembj/xP1Ofa22+J517Tb/L\nxAVwxg1Q0ZB+yj5BGAKKSNydYlxeW2bKcQf2vuFKR9wVPQW/yuvhU48Zj/S1P0HHHnP8jU+Y29ca\n1+w6KbZMWdI+CoQ94l48p1roJ0olO9wtNQc5C4M8qlQQMlA8ipNwd6i2m46q/3zlwDuZbKfne34C\nR380uX7ikeaxrM547k9+F576rqmpPe8Drtd7PPe4U8c7EE5NXRNbRhCEIaR4xN3dodrdbrzIyoYD\nf19rq2SqZV1Wb2bgWfeQ8eA/9mBqSprXc1fOICdv0SnpUBUEYQjJyQhWSp2rlFqrlNqglLo+zfbL\nlVKvK6XeUEo9r5Q6Iu8tdXvu0Y78pW3ZyDvT0O6yOjNP5s43jRfvzTUOeTx371Byi0TugiAMIX2K\nu1LKD/wcOA+YC1ymlJrr2e1t4FSt9XzgW8Ct+W5oSrbM/m35y/m1EXum9MryemjfZdLTJh3Ve7vP\nl6yDHSxL5kZ7LwISuQuCMITkErkvAjZorTdqrbuBu4CL3DtorZ/XWtuUk6XAZPKNtWXW/gM2P5vq\nex8Ix11jHqdm6Jh1lxKedHT6faw1Eyxx1QnxRu6SLSMIwtCRi+JMAra6njc66zLxCeChdBuUUouV\nUi8ppV7avXt37q2EpLhveMx0gp7w6f69PhNTj4Wv70+tH+Omwhk9OPt8MyglHTb6D5ZlLjolkbsg\nCENIXjtUlVKnY8Q97VA3rfWtOJbNwoULdbp9MmJtmXjURMeZKjrmm3nvN6I+47TM+9jIPVCSOh+m\nG0mFFARhCMlFcbYB7sItk511KSilDgduB87TWjd5tx8wNnJPRAd/Rho3wVIzmCkbNp3SXc/d67lL\nh6ogCENILrbMcmCmUmq6UioEXAo84N5BKTUVuBf4sNZ6Xb9a8OyPYeNTfe9ns2US8cLzr3s899LM\nnrvYMoIgDCF9Ru5a65hS6lrgYcAP3KG1flMpdbWz/Rbgq0AdcLMyud0xrfXCPo/etR8e+4bxs2ec\nmn1fty1TaFGw9dxTbBmP515oFyRBEEY0ORnBWuslwBLPultcy58EPtnvo29ZauyWXatyaIS1ZWKF\nJ5ShcpMnb+tvQ5psGdX7dYIgCIPE8KqkraK39+3086K66RmhGi88oRw3F8YdapYDHs/9sj/DvIuH\np12CIIxahlfcG1+ip2DX7jXZ97W2DBSef338NbD4SbMcqjCFxepnm+ezz4WLfzVcLRMEYZQyvOIe\naYP6mWZ51+rs+9rIHQrPlnHjD8JnXoW5Fw53SwRBGMUMr0rGu6HOEfeWXtmVqWhX5F5oHaqCIAgF\nxvCKeyJq0gd9wb6nznPbMoUcuQuCIBQAwxy5R012SagsOUlvJty2TKF57oIgCAXG8Nsy/qCpyRJp\ng9UPQiKRYWdXtYJCy5YRBEEoMAojcg+Wwqt/hD9fDi/e0vfrxHMXBEHISgGIuxO528h8/cN9v048\nd0EQhKwUiC3jmgVp2yt9v048d0EQhKwMf7aMtWUskf2m5kw2JHIXBEHIyvCqpE6YNEjvfKhdLanP\nvZ2s4rkLgiBkZfjE3aY2+tOIO555PBLR1OcSuQuCIGRlGFXSEXB/qLe4a4+4xz3i7hNxFwRByMYw\nRu5ucS/Nvm8ilvpcIndBEISsFIC4B9KIu9eW8Yq7eO6CIAjZKE5bRiJ3QRCErBRA5J7Olukrchdx\nFwRByEYBRO7psmU8eLNlZBCTIAhCVoY/cvcFTVXIdNsscYncBUEQ+sPwi7vblgmWp99XbBlBEIR+\nUVi2TLgi/a4yiEkQBKFfFMgIVSdyDzni7rVlvJG7eO6CIAhZCQzfoV22jD9klsOVqdssvWrLSOQu\nCIKQjeETd+2yZXxOM6y4eyN3LYXDBEEQ+kMBeO4hqJwAgRKonZF9X4tE7oIgCFkZ/mwZXxDK6+G6\nzTD9FLvRs68ncpfCYYIgCFnJSSWVUucqpdYqpTYopa5Ps10ppW5ytr+ulDqqzzd12zIAwZLkxNe9\nbBmJ3AVBEPpDnyqplPIDPwfOA+YClyml5np2Ow+Y6fwtBn7R96FdtkzyaNn37dlNPHdBEIRs5BIC\nLwI2aK03aq27gbuAizz7XAT8ThuWAtVKqQlZ39Ubuadu9DyVbBlBEIT+kItKTgK2up43Ouv6u4+H\nNOKeqy0jee6CIAhZGdIQWCm1WCn1klLqpfa2NrMyF1tGIndBEIR+kYtKbgOmuJ5Pdtb1dx+01rdq\nrRdqrReWlzslB1LEvWfP7M/FcxcEQchKLuK+HJiplJqulAoBlwIPePZ5APiIkzVzHLBfa70j67v2\npEK6xlHlnC2TqeNVEARBgBxGqGqtY0qpa4GHAT9wh9b6TaXU1c72W4AlwPnABqADuLLPI+uEyXFP\nEWq7LKmQgiAIB0JO5Qe01kswAu5ed4trWQPX9O/QurclkzEilw5VQRCE/jC8I1T9Ga4tMohJEATh\ngBje2jK9OlMz2TJSOEwQBKE/DHPkPkBbRiJ3QRCErAxz5J5udCp9l/wVz10QBCErwxu5+7zinmu2\njKRCCoIgZGP4xN3nhzGeCgWZ8txlEJMgCEK/GD5xHzMFPnK/Z6WUHxAEQcgHBaqSUjhMEAThQCgs\nce+xZTzrJXIXBEHoFwWmkjJZhyAIQj4oMHG3yAhVQRCEA6GwVDLnyToKq9mCIAiFRoGpZIY8957n\nznaJ3AVBELKidK+c8iE6sFKtwNphOfjAqAf2DHcj+oG0d3CR9g4+xdbmoWrvQVrrsX3tlFPJ30Fi\nrdZ64TAev18opV6S9g4e0t7BpdjaC8XX5kJrr/gbgiAIIxARd0EQhBHIcIr7rcN47IEg7R1cpL2D\nS7G1F4qvzQXV3mHrUBUEQRAGD7FlBEEQRiDDIu5KqXOVUmuVUhuUUtcPRxv6Qim1SSn1hlLqVaXU\nS866WqXUv5RS653HmmFs3x1KqV1KqZWudRnbp5T6onO+1yqlzimQ9n5dKbXNOcevKqXOL6D2TlFK\nPaGUWqWUelMp9RlnfUGe4yztLchzrJQqUUotU0q95rT3G876Qj2/mdpbkOcXAK31kP4BfuAtYAYQ\nAl4D5g51O3Jo5yag3rPu+8D1zvL1wPeGsX2nAEcBK/tqHzDXOc9hYLpz/v0F0N6vA59Ps28htHcC\ncJSzXAmsc9pVkOc4S3sL8hxjRiRWOMtB4EXguAI+v5naW5DnV2s9LJH7ImCD1nqj1robuAu4aBja\nMRAuAn7rLP8WeO9wNURr/TSw17M6U/suAu7SWke01m8DGzD/hyEjQ3szUQjt3aG1ftlZbgVWA5Mo\n0HOcpb2ZGO72aq11m/M06PxpCvf8ZmpvJob9Ozwc4j4J2Op63kj2L+FwoYFHlVIrlFKLnXUNWusd\nzvI7QMPwNC0jmdpXyOf800qp1x3bxt6CF1R7lVLTgCMx0VrBn2NPe6FAz7FSyq+UehXYBfxLa13Q\n5zdDe6FAz690qGbmJK31AuA84Bql1CnujdrcexVsqlGht8/hFxh7bgGwA/i/4W1Ob5RSFcA9wH9p\nrVvc2wrxHKdpb8GeY6113PmNTQYWKaXmebYX1PnN0N6CPb/DIe7bgCmu55OddQWF1nqb87gLuA9z\nS7VTKTUBwHncNXwtTEum9hXkOdda73R+MAngNpK3rQXRXqVUECOUf9Ra3+usLthznK69hX6OAbTW\nzcATwLkU8Pm1uNtbyOd3OMR9OTBTKTVdKRUCLgUeGIZ2ZEQpVa6UqrTLwNnASkw7P+rs9lHAOwns\ncJOpfQ8Alyqlwkqp6cBMYNkwtC8F+yN2eB/mHEMBtFcppYBfAau11j90bSrIc5ypvYV6jpVSY5VS\n1c5yKXAWsIbCPb9p21uo5xcY+mwZpyf5fExv/lvAl4ejDX20bwamp/s14E3bRqAOeAxYDzwK1A5j\nG/+EuQ2MYvy8T2RrH/Bl53yvBc4rkPb+HngDeB3zY5hQQO09CWMJvA686vydX6jnOEt7C/IcA4cD\nrzjtWgl81VlfqOc3U3sL8vxqrWWEqiAIwkhEOlQFQRBGICLugiAIIxARd0EQhBGIiLsgCMIIRMRd\nEARhBCLiLgiCMAIRcRcEQRiBiLgLgiCMQP4/sPn7mFSaOwUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195c0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=3), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dkcUidxXKT2P0++R2/cMPuF56Tty7zOsaJptAocCWCZlaRMO1ZYaLjdz9kXCs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\nC4IgVCEi7oIgCFXI/wec5rjkoeKdYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc19a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=5), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ajZ50itx9WuRe0JvWu0Xu475j+vIdIVVh1znqQm+BVjem53aovnI1sDCNAhGP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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e01d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=20), X_train, X_test, y_train, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
